Finding Morphology Points of Electrocardiographic-Signal Waves Using Wavelet Analysis

Abstract

A new algorithm has been developed for delineation of significant points of various electrocardiographic signal (ECG) waves, taking into account information from all available leads and providing similar or higher accuracy in comparison with other modern technologies. The test results for the QT database show a sensitivity above 97% when detecting ECG wave peaks and 96% for their onsets and offsets, as well as better positive predictive value compared to the previously known algorithms. In contrast to the previously published algorithms, the proposed approach also allows one to determine the morphology of waves. The segmentation mean errors of all significant points are below the tolerances defined by the Committee of General Standards for Electrocardiography (CSE).

Cyberheart-Diagnostics Software Package for Automated Electrocardiogram Analysis Based on Machine Learning Techniques

“…All 1652 ECG records were interpreted by expert physicians (cardiologists and physicians of functional diagnostics), who then formed structured medical conclusions. Subsequently, the ECG records were analyzed automatically using key point detection (KPD), segmented and automatically described in the form of a pre-hospital report with the software developed by the authors according to the classical criteria for ECG analysis [5].…”

“…The automated ECG analysis software was trained using machine learning techniques on attributes obtained with KPD ( Figure 2). More information about the KPD algorithm is provided in work [5].…”

LUDB: A New Open-Access Validation Tool for Electrocardiogram Delineation Algorithms

“…As the case study, we made use of this dataset for validating our recent algorithm [14], that implements wavelet transform for multi-lead multi-morphology analysis with error correction, and make a comparison to the popular ecg-kit tool [15], which employs one of its predecessors, a singlelead delineator [4]. Expectedly, the results demonstrate a comparable performance of both for QTDB and a noticeable improvement of delinearing P and T waves for LUDB achieved by the former algorithm.…”

“…In Section I, we describe the LUDB database. Section II contains an outline of the delineation algorithm [14]. A case study of its validation with LUDB and QTDB is reported in Section III.…”

Artificial Neural Networks in Cardiology: Analysis of Numerical and Text Data

“…При оценке их состояния учитывали возраст, пол, индекс массы тела, общий холестерин, холестерин липопротеинов высокой плотности, систолическое артериальное давление, диастолическое артериальное давление, триглицерид, гемоглобин, заболевания щитовидной железы, хронические почечная недостаточность, гепатит B, гепатит C, цирроз печени, курение и диабет. По данным, которые предоставили авторы, наилучшая производительность была получена при исключении семи параметров (пол, гемоглобин, заболевания щитовидной железы, хронические почечная недостаточность, гепатиты B и C, цирроз печени) с точностью прогнозирования в 81.2 %.Использование ИНС в области обработки ЭКГ рассматривается с точки зрения применения вейвлет преобразований[25] с точностью определения P-волн, QRSкомплексов и T-волн в среднем 97.5 %, 98.4 % и 97.2 %, соответственно. Однако, применение ИНС позволяет увеличить скорость и качество анализа этих данных.…”